Technical Field
[1] Described embodiments relate to systems and computer implemented methods for
capital management.
Background
[2] Effective capital management enables business or trading entities to ensure adequate
access to funds necessary for operational expenses while making sure that the entity's assets are
invested in the most financially productive manner. In fact, the majority of businesses that fail,
do so because of cash flow problems.
[3] Capital management may involve consideration of a wide range of factors including:
outstanding receivables, obsolete inventory, cost of short term debt, payment obligations,
liquidity and trading obligations of trading partner entities, short term investment yields.
Taking into account the large range of dynamic factors relevant for effective capital
management is a computationally complex, time and labour intensive operation, and can be an
arduous and error prone process.
[ 4] It is desired to address or ameliorate some of the disadvantages associated with prior
methods and systems for processing images for docket detection and information extraction, or
at least to provide a useful alternative thereto.
[5] Any discussion of documents, acts, materials, devices, articles or the like which has
been included in the present specification is not to be taken as an admission that any or all of
these matters form part of the prior art base or were common general knowledge in the field
relevant to the present disclosure as it existed before the priority date of each claim of this
application.
[6] Throughout this specification the word "comprise", or variations such as "comprises"
or "comprising", will be understood to imply the inclusion of a stated element, integer or step,
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or group of elements, integers or steps, but not the exclusion of any other element, integer or
step, or group of elements, integers or steps.
Summary
[7] Some embodiments relate to a computer-implemented method comprising:
determining a dataset of transactions occurring during a first time period; determining at least a
one subset of related transactions from the dataset of transactions by: identifying a first group
of transactions from the dataset of transactions, wherein the transactions of the first group have
one or more common attributes; determining a interval for each of the at least one subsets, each
of the intervals being indicative of a periodicity of the respective one of the at least one subsets;
and generating a model of periodic transactions, the model including, for each of the at least
one subsets: (i) the interval; and (ii) at least one of the common attributes.
[8] Identifying a first group of transactions from the dataset of transactions may comprise
identifying transactions between a first entity and one or more second entities. Identifying a
first group of transactions from the dataset of transactions may comprise identifying
transactions between a first account associated with a first entity and one or more second
entities. Identifying a first group of transactions from the dataset of transactions may comprise
identifying transactions with substantially similar payment amounts.
[9] In some embodiments, determining the at least one subset of related transactions from
the dataset of transactions may comprise: identifying a second group of transactions from the
first group of transactions, wherein the second group of transactions each relate to transactions
with substantially similar payment amounts. In other embodiments, determining the at least
one subset of related transactions from the dataset oftransactions may comprise: identifying a
second group of transactions from the dataset of transactions, wherein the second group of
transactions each relate to transactions between a first entity and one or more second entities.
[ 1 0] The method may further comprise rounding the payment amount of each of the first
group of transactions or of the dataset of transactions prior to identifying the second group of
transactions.
[11] The one or more common attributes comprise one or more of: (i) account name; (ii)
contact; (iii) payment amount.
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[12] In some embodiments, determining the interval for each of the at least one subsets
may comprise determining that a regularity of an occurrence of the related transactions of the at
least one subset of transactions throughout the first time period meets a regularity threshold.
Determining that a regularity of an occurrence of the transactions of the at least one subset of
transactions throughout the first time period meets a regularity threshold may comprise:
determining, for each related transaction of the at least one subset of transactions, a number of
days between the related transaction and a next occurring related transaction based on a date of
the related transaction and the next occurring related transaction; determining a standard
deviation value of the numbers of days for the at least one subset of transactions; and
determining that the standard deviation value meets the regularity threshold. In some
embodiments, determining the interval for each of the at least one subsets comprises comparing
a distribution of individual intervals of the related transactions with one or more distribution
models indicative of periodicity to determine a best fit.
[13] The method may further comprise determining that a time span covered by the related
transactions of the at least one subset throughout the first time period meets a coverage
threshold.
[ 14] The method may further comprise predicting one or more instances of future
transactions using the model. For example, the model may predict, for each recurring
transaction, one or more of the following attributes of the transaction: (i) payment amount; (ii)
regularity of payment; (iii) day(s), week(s), month(s), and/or year(s) on which payment is
predicted to be paid; (iv) account to and/or from which payment is predicted to be made; and
(v) contact to and/or from which payment is predicted to be made.
[15] Some embodiments relate to a computer implemented method comprising: assessing
financial data associated with a primary entity during a time frame; predicting recurring
transactions during the time frame; determining a baseline cash flow forecast for the primary
entity for the time frame based at least one the predicted recurring transactions; determining
predicted capital surplus and/or capital deficit during the time frame based at least on the
baseline cash flow forecast; determining one or more sub-time frames of the time frame where
cash flow is predicted to be in surplus or deficit; based on the determined sub-time frames,
generating by a recommendation engine, one or more recommendations to improve capital
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management for the primary entity. In some embodiments, predicting recurring transactions
during the time frame comprises performing the any one of the described methods.
[16] In some embodiments, determining the baseline cash flow forecast may comprise:
determining a trend component based on the financial data associated with the primary entity
for that time frame; determining a first seasonality component based on the financial data; and
projecting the trend component and the first seasonality component to the time frame to
determine the baseline cash flow forecast. In some embodiments, determining the baseline
cash flow forecast may comprise: determining the baseline cash flow forecast comprises:
determining the first seasonality component based on a first periodicity in the financial data;
determining a fitness metric by comparing the financial data with the trend model and the
seasonality component; responsive to the determined fitness metric being below a
predetermined fitness threshold, determining a second seasonality component based on a
second periodicity in the financial data; and projecting the trend component and the second
seasonality component to the time frame to determine the baseline cash flow forecast.
[ 1 7] The method may further comprise generating a cash flow forecast tool within a
graphical user interface (GUI) of a display device, wherein the cash flow forecast tool depicts a
representation of the baseline cash flow forecast and the one or more recommendations. The
method may further comprise: in response to detecting user selection of one or more of the one
or more suggestions using the cash flow forecast tool, adjusting the financial data in accordance
with the selected one or more suggestions; determining a modified cash flow forecast for the
time frame based on the adjusted financial data; and depicting a representation of the modified
cash flow forecast within the cash flow forecast tool. In some embodiments, the method may
further comprise: automatically executing at least one of the recommendations based on target
parameters; analysing the automatically executed recommendations to determine whether the
executed capital management recommendations mitigated the forecasted one or more cash flow
shortfall periods or one or more cash flow excess periods; and revising the recommendation
engine based on the analysis.
[18] Some embodiments relate to a system comprising: one or more processors; and a
memory in communication with the one or more processor, the memory comprising program
code which when executed by the one or more processors configures the one or more
processors to perform any of the described methods.
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[19] Some embodiments relate to a machine-readable medium storing computer readable
code, which when executed by one or more processors is configured to perform any one of the
described methods.
[20] Some embodiments relate to a computer implemented method for managing capital
for a primary entity, the method comprising: determining a baseline cash flow forecast for a
time frame based on financial data associated with the primary entity for that time frame;
determining one or more sub-time frames of the time frame where cash flow is predicted to be
in surplus or deficit; based on the determined sub-time frames, generating by a
recommendation engine one or more recommendations to improve capital management for the
primary entity, wherein determining the baseline cash flow forecast for the time frame
comprises assessing the financial data to determine predicted capital surplus and/or capital
deficit within the time frame.
[21] In some embodiments, the financial data comprises transactions between the primary
entity and one or more other entities.
[22] In some embodiments, determining the baseline cash flow forecast comprises:
determining a trend component based on the financial data associated with the primary entity
for that time frame, determining a first seasonality component based on the financial data,
projecting the trend component and the first seasonality component to the time frame to
determine the baseline cash flow forecast.
[23] In some embodiments, determining the baseline cash for forecast comprises:
determining the first seasonality component based on a first periodicity in the financial data;
determining a fitness metric by comparing the financial data with the trend model and the
seasonality component; if the determined fitness metric is below a predetermined fitness
threshold, determining a second seasonality component based on a second periodicity in the
financial data; and projecting the trend component and the second seasonality component to the
time frame to determine the baseline cash flow forecast.
[24] In some embodiments, the first periodicity is larger than the second periodicity.
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[25] In some embodiments, the method further comprises predicting a likelihood or
probability of receiving payment of each invoice having a due date in the time frame and
determining a modified cash flow forecast for the time frame based on the predictions.
[26] In some embodiments, predicting a probability of receiving payment of each invoice
having a due date in the time frame comprises determining an action score for a secondary
entity associated with each invoice, wherein the action score is based on historic payment
behaviours of the respective secondary entity, and determining the probability of the second
entity paying the invoice within a given payment period, wherein the given period falls within
the time frame.
[27] In some embodiments, the given period is a specific day within the time frame.
[28] In some embodiments, the method further comprises generating a cash flow forecast
tool within a graphical user interface (GUI) of a display device, wherein the cash flow forecast
tool depicts a representation of the baseline cash flow forecast and the one or more
recommendations.
[29] In some embodiments, the one or more recommendations comprise recommendations
to adjust the financial data to manage the predicted cash flow during the one or more sub-time
frames.
[30] In some embodiments, in response to detecting user selection of one or more of the
one or more suggestions using the cash flow forecast tool, adjusting the financial data in
accordance with the selected one or more suggestions, determining a modified cash flow
forecast for the time frame based on the adjusted financial data and depicting a representation
of the modified cash flow forecast within the cash flow forecast tool.
[31] In some embodiments, adjusting financial data comprises modifying a due date for
payment on an invoice.
[32] In some embodiments, the recommendations include one or more of: seeking early
payment of an outstanding invoice, seeking an extended term for payment of an outstanding
invoice, subscribing to a financial product, varying terms of at least one of the one or more
transactions.
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[33] In some embodiments, the recommendations are generated based on one or more
capital management target parameters comprising: debt to equity ratio for the entity, weighted
average cost of capital to the entity, debt coverage ratio for the entity, number of debtor days
for the entity or number of creditor days for the entity.
[34] In some embodiments, the method further comprises automatically executing at least
one of the recommendations based on the target parameters.
[35] In some embodiments, the method further comprises analysing the automatically
executed recommendations to determine whether the executed capital management
recommendations mitigated the forecasted one or more cash flow shortfall periods or one or
more cash flow excess periods; and revising the recommendation engine based on the analysis.
[36] In some embodiments, the recommendation engine comprises one or more machine
learning models and the revising the recommendation engine comprises training the machine
learning model based on the analysis.
[37] In some embodiments, the financial data comprises one or more of: bank account
transaction data, invoice data, billings data, expense claim data, quote data, sales data, purchase
order data, receivables data, transaction reconciliation data, balance sheet data, profit and loss
data, or payroll data.
[38] In some embodiments, the financial data comprises one or more financial records and
each financial record comprises a transaction amount, a transaction date and one or more entity
identifiers.
[39] Some embodiments relate to a system for managing capital for a primary entity, the
system comprising: one or more processors; a memory in communication with the one or more
processor, the memory comprising program code which when executed by the one or more
processors configures the one or more processors to: determine a baseline cash flow forecast
for a time frame based on financial data associated with the primary entity for that time frame;
determine one or more sub-time frames of the time frame where cash flow is predicted to be in
surplus or deficit; based on the determined sub-time frames, generating by a recommendation
engine one or more recommendations to improve capital management for the primary entity,
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wherein determining the baseline cash flow forecast for the time frame comprises assessing the
financial data to determine predicted capital surplus and/or capital deficit within the time frame.
[ 40] In some embodiments, the one or more processors are further configured to predict a
likelihood or probability of receiving payment of each invoice having a due date in the time
frame and determining a modified cash flow forecast for the time frame based on the
predictions.
[ 41] In some embodiments, the one or more processors are further configured to generate a
cash flow forecast tool within a graphical user interface (GUI) of a display device, wherein the
cash flow forecast tool depicts a representation of the baseline cash flow forecast and the one or
more recommendations.
[ 42] In some embodiments, the one or more processors are further configured to
automatically execute at least one of the recommendations based on the target parameters.
[ 43] In some embodiments, the one or more processors are further configured to analyse
the automatically executed recommendations to determine whether the executed capital
management recommendations mitigated the forecasted one or more cash flow shortfall periods
or one or more cash flow excess periods; and revising the recommendation engine based on the
analysis.
[44] Some embodiments relate to a machine-readable medium storing computer readable
code, which when executed by one or more processors is configured to perform the method
according to any one of the embodiments.
Brief Description of Drawings
[ 45] Figure 1 is a schematic diagram of a process for using a capital management platform
to improve capital management of an entity, according to some embodiments;
[ 46] Figure 2 is a block diagram of the components of a capital management component,
according to some embodiments;
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[ 4 7] Figure 3 is an example screenshot of a visual display of a capital management tool
provided by the capital management component of Figure 2, according to some embodiments;
[48] Figure 4 is a process flow diagram of a method for forecasting cash flow for an entity,
according to some embodiments;
[ 49] Figure 5 illustrates a first and a second cash flow forecast chart obtained by
implementing the process flow of Figure 4;
[50] Figure 6 is a block diagram depicting an example capital management platform,
according to some embodiments;
[51] Figure 7 is a process flow diagram of a method for managing capital for an entity,
according to some example embodiments;
[52] Figure 8 is a block diagram depicting an example application framework, according
to some embodiments;
[53] Figure 9 is a block diagram depicting an example hosting infrastructure, according to
some embodiments;
[54] Figure 10 is a block diagram depicting an example data centre system for
implementing described embodiments;
[55] Figure 11 is a block diagram illustrating an example of a machine arranged to
implement one or more described embodiments;
[56] Figure 12 is a process flow diagram of a method for generating models for predicting
recurring transactions associated with entities, according to some embodiments;
[57] Figure 13 is a process flow diagram of a method for determining a subset of related
transactions from a dataset of transactions, according to some embodiments;
[58] Figure 14 is a schematic of an example of an application ofthe methods of Figure 12
and Figure 13; and
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[59] Figure 15 is a process flow diagram of a method for managing capital for an entity,
according to some embodiments.
Description of Embodiments
[60] Described embodiments relate to systems, computer implemented methods and
computer programs for capital management, and in some embodiments, cash flow forecasting.
[ 61] In some embodiments, a capital management platform, such as a cash flow
forecasting platform or tool, is provided. The capital management platform is configured to
determine predicted capital shortfalls and/or capital surpluses of an entity for a given period of
time. The capital management platform may be configured to generate, on a user interface, a
visual display of a predicted cash flow of the entity for the period of time based on the
predicted capital shortfalls and/or capital surpluses. For example, the visual display may
comprise a graphical representation of the predicted cash flow for each day of the time period.
An example of such a graphical representation is presented is Figs 3 to 5, and discussed in more
detail below.
[62] The capital management platform may be configured to determine the predicted
capital shortfalls and/or capital surpluses at a particular point or day in a given time period
based on an assessment of financial data associated with the entity. Financial data associated
with an entity may comprise banking data, such as banking received via a feed from a financial
institution, accounting data, payments data, assets related data, transaction data, transaction
reconciliation data, bank transaction data, expense data, tax related transaction data, inventory
data, invoicing data, payroll data, purchase order data, quote related data or any other
accounting entry data for an entity. The financial data may comprise one or more financial
records. Each financial record may comprise a transaction amount, a transaction date, one or
more due dates and one or more entity identifiers identifying the entities associated with the
transaction. For example, financial data relating to an invoice may comprise a transaction
amount corresponding to the amount owed, a transaction date corresponding to the date on
which the invoice was issued, one or more payment due dates and entity identifiers indicating
the invoice issuing entity and the entity under the obligation to pay the invoice. Financial data
may also comprise financial records indicating terms of payment and other conditions
associated with the financial transaction associated with the financial data.
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[63] In some embodiments, the capital management platform may be configured to predict
capital shortfalls and/or capital surpluses for a primary entity over a time period based on data
relating to historical or current payment behaviour or patterns of related entities, counterparties
in transactions or third parties. In a highly interdependent trading environment, the further
capital surplus or shortfall position of an entity may depend on the debt obligations or liquidity
positions of the counterparties that the entity trades with or even third parties that the entity
may not directly trade with. In some embodiments, the capital management platform may have
access to data relating to historical payment behaviour of such counterparties or third parties to
generate a more informed prediction of capital shortfalls and/or capital surpluses for the
primary entity.
[ 64] In some embodiments, capital management platform may be configured to generate
suggestions or recommendations for more effective capital management based on the predicted
capital shortfalls and/or capital surpluses. In some embodiments, capital management platform
may be configured to take one or more actions for more effective capital management based on
the suggestions or recommendations.
[65] Examples merely typify possible variations. Unless explicitly stated otherwise,
components and functions are optional and may be combined or subdivided, and operations
may vary in sequence or be combined or subdivided. In the following description, for purposes
of explanation, numerous specific details are set forth to provide a thorough understanding of
example embodiments. It will be evident to one skilled in the art, however, that the present
subject matter may be practiced without these specific details.
[66] Figure 1 illustrates a process 100 for using a capital management tool to improve
capital management of an entity. In some embodiments, a capital management platform 102
may be provided to one or more client devices by one or more servers executing program code
stored in memory. The capital management platform 102 may provide the capital management
tool 104 for use by users of the one or more client devices. In some embodiments, the capital
management platform 102 is arranged to communicate with a database 106 comprising
financial information associated with a network of entities associated with the capital
management platform, and may, for example, include accounting data for transactions between
two or more entities. Accordingly, analysis of the data allows for inferences about the business
interactions or transactions of those entities. For example, computational analysis of historical
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patterns of transactions between entities and trading behaviours of entities including
responsiveness to financial obligations, may be used to predict behaviours of the entities.
[ 6 7] In some embodiments, database 106 may be part of an accounting system, such as a
cloud based accounting system configured to enable entities to manage their accounting or
transactional data. The accounting or transactional data may include data relating to bank
account transactions or transfers, invoice data, billings data, expense claim data, historical cash
flow data, quotes related data, sales data, purchase order data, receivables data, transaction
reconciliation data, balance sheet data, profit and loss data, payroll data, for example. Data in
database 106 may enable identification of interrelationships between the primary entity and
other entities based on the transactional data. The interrelationships may include relationships
that define payment or debt obligations, for example. Based on the interrelationships between
the primary entity and other entities, data in database 106 may be used to identify one or more
networks of related entities that directly or indirectly transact with each other. Within a network
of entities, the financial or cash flow position of one entity may have an impact on the financial
or cash flow position of the rest of the entities in the network.
[68] The capital management platform 102 may provide accounting tools to a particular
entity managing accounting for one or more businesses, as discussed in more detail below with
reference to Figure 6.
[69] The capital management tool104 may be provided by one or more processors of the
capital management platform 102 executing program code of a capital management component
108. The capital management component 108 may comprise a cash flow forecasting engine
110 and a recommendations and actions engine 112. The cash flow forecasting engine 110,
when executed by the one or more processors of the capital management platform 102, may be
configured to predict capital shortfalls and/or capital surpluses of an entity for a given period of
time based on information derived from the database 106. For example, the cash flow
forecasting engine 110 may predict baseline capital shortfalls or baseline capital surpluses
based on payment terms of transaction data, such as invoices.
[70] In some embodiments, the cash flow forecasting engine 110 may predict or otherwise
determine the probability or likelihood of payments being received during a particular time
frame and taking that information into consideration when predicting capital shortfalls and/or
capital surpluses of the entity for the given period of time. For example, the cash flow
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forecasting engine 110 may enhance the predicted baseline capital shortfalls or baseline capital
surpluses by generating a modified prediction for the shortfalls and/or surpluses based on
knowledge about the creditworthiness, a credit or trade score and/or payment history of the
entity associated with the transaction data. For example, if an invoice is due to be paid on 15
March 2020 according to the payment terms, but it is known from the payment history of the
entity to which the invoice was issued that that entity always pays invoices at least 10 days late,
that information can be used by the cash flow forecasting engine 110 to adjust the predicted
capital shortfalls and/or surpluses for the time period to account for the predicted late payment.
In some embodiments, a credit or trade score for a plurality or network of entities may be
determined using the techniques described in PCT/US2017/045351, the entire content of which
is incorporated herein by reference.
[71] The recommendations and actions engine 112 when executed by the one or more
processors of the capital management platform 102, may be configured to determine
recommendations based on the predicted capital shortfalls and/or capital surpluses and in some
cases, to take appropriate actions. For example, such appropriate actions may comprises
modifying payment terms, for example, bringing forward due dates on invoices for payers 114,
deciding to incur penalty costs for later payment of invoices to payees 116, seeking financing,
or investing surplus funds, for example, via a marketplace server 118 in communication with
the capital management platform 102.
[72] The capital management tool 104 may be configured to generate, on a user interface,
a visual display of a predicted cash flow of the entity for the period of time based on the
predicted capital shortfalls and/or capital surpluses. For example, the visual display may
comprise a graphical representation of the predicted cash flow for each day of the time period.
Example screenshots of the visual display of the capital management tooll04 are shown in
Figures 3 to 5.
[73] Referring now to Figure 2, there is shown a block diagram of the modules,
components or engines of the capital management component 102. As illustrated in Figure 1,
the capital management component 102 comprises the cash flow forecast engine 110 and the
recommendations and actions engine 112.
[74] As shown in Figure 2, the cash flow forecast engine 110 may comprise a plurality of
components to predict capital shortfalls and/or capital surpluses of an entity for a given period
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oftime. The cash flow forecast engine 110 may also make iterative updates to predicted or
determined cash flow forecasts based on information received from other components of the
capital management component 102.
[75] The cash flow forecast engine 110 may comprise a payables accrual logic engine 200
configured to analyse data relating to payment obligations of an entity to predict future payable
obligations for the entity. The payables accrual logic engine 200 may employ a predictive
model such as a regression model or a trained neural network for example and historical
payables data for the entity to predict future payable obligations for the entity during a time
period of interest.
[76] The cash flow forecast engine 110 may comprise a recurring cash account logic
engine 202 configured to analyse data relating to cash transactions, which may include petty
cash transactions, to predict future cash transactions for the entity. Data relating to cash
transactions includes data of payables or receivables in cash that an entity may engage in. The
recurring cash account logic engine 202 may employ a predictive model such as a regression
model or a trained neural network for example and historical data relating to cash transactions
in order to predict future cash transactions that may be recurring during a given period.
[77] The cash flow forecast engine 110 may comprise a tax prediction engine 204
configured to analyse accounting entry data to predict future tax obligations of an entity. The
tax prediction engine 204 comprises jurisdiction specific taxation calculation logic to assess or
estimate future tax obligations of an entity based on an assessment of revenue and expenses
using financial information, such as accounting entry data retrieved from database 106, for
example.
[78] The cash flow forecast engine 110 may comprise a payroll prediction engine 206
configured to analyse financial information, such as accounting data, related to payroll for an
entity. Entities may operate with varying levels of workforce due to several factors such as
seasonality, inventory levels or market conditions, for example. The payroll prediction engine
206 may employ a predictive model, such as a regression model or a trained neural network for
example, and historical payroll related data in order to predict future payroll obligations for a
given period.
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[79] The cash flow forecast engine 110 may comprise a receivables accrual logic engine
208 configured to analyse financial information, such as accounting data, related to receivables
for payment obligations owed to the entity by other entities. The receivables accrual logic
engine 208 may employ a predictive model, such as a regression model or a trained neural
network for example, and historical receivables data in order to predict future receivables for a
given period.
[80] The cash flow forecast engine 110 may be configured to determine a cash flow
forecast based on outputs from one or more of the payables accrual logic engine 200, a
recurring cash account logic engine 202, the tax prediction engine 204, payroll prediction
engine 206 and/or cash flow forecast engine 110. In some embodiments, the cash flow forecast
engine 110 may be configured to determine a baseline cash flow based on these outputs and, in
some embodiments, to generate a graphical display for displaying the cash flow forecast to user
on a user interface of a client device.
[81] In some embodiments, the cash flow forecast engine 110 may be configured to
identify recurring transactions in a database of transactions (for example, past transactions) and
generate a model for predicting future recurring transactions. Predicted recurring transactions
for a given period may then be used by the cash flow forecast engine 110 in determining or
predicting a baseline cash flow forecast.
[82] The recommendations and actions engine 112 is configured to receive cash flow
forecast information from the cash flow forecast engine 110 for a given time frame and to
generate one or more recommendations and/or to take one or more actions to improve capital
management. The recommendations and actions engine 112 may determine one or more subtime
frames of the time frame wherein the entity is predicted to have capital shortfalls and/or
capital surpluses. For example, the recommendations and actions engine 112 may compare the
entity's cash levels at a particular time, for example, each day of the time frame, and compare
the determined cash level to a threshold, for example, a shortfall threshold and/or a surplus
threshold, to determine whether the entity will have shortfall or surplus at that time.
[83] If the cash flow forecast engine 110 determines that the entity will have a shortfall for
a sub-time frame ofthe time frame, the recommendations and actions engine 112 may
determine one or more recommendations to increase the cash flow for that sub-time frame. The
recommendations and actions engine 112 may recommend that payment terms of invoices
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(either issued and unpaid or yet to be issued) be modified to increase the probability or
likelihood of having increased cash flow for the sub-time frame. For example, the
recommendations and actions engine 112 may bring forward due dates on those invoices, offer
an incentive to the payee to pay the invoice early and/or penalise the payee for any later
payment. The recommendations and actions engine 112 may cause the generation of invoices
based on the recommendations for sending to the payees, for example, by issuing an instruction
to an invoice generation engine (not shown). The recommendations and actions engine 112
may determine that it is appropriate to postpone payments, such as paying one or more invoices
at a time later than the due date.
[84] The recommendations and actions engine 112 may recommend that outstanding
receivables be followed up on and may generate an email reminder ready for sending to the
payee, for example.
[85] The recommendations and actions engine 112 may recommend that short term
finance be accessed to cover periods of cash flow shortfalls.
[86] If the cash flow forecast engine 110 determines that the entity will have excess cash
flow for a sub-time frame of the time frame, the recommendations and actions engine 112 may
determine one or more recommendations to better utilise the cash flow, for example, by
recommending appropriate short term investments.
[87] In some embodiments, the recommendations and actions engine 112 comprises a
quoting and pricing engine 210 configured to communicate with the marketplace server 118 to
provide quotes and prices for financial products made available to an entity in response to a
recommendation involving accessing short term debt or making short term investments. For
example, if the recommendations and actions engine 112 suggests that an entity seek short term
debt for a future period of capital shortfall, the quoting and pricing engine 210 may
communicate the various attributes of the recommended short term debt to the marketplace
server 118. The attributes of the suggested short term debt may include the amount of debt, the
term of the debt, for example. The marketplace server 118 may make available the various
attributes of the suggested short term debt to the marketplace and seek bids for offers ofloans
in response to the various parameters. The marketplace server 118 may communicate the
various offers to the quoting and pricing engine 210. The quoting and pricing engine 210 may
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make the various offers available to the entity as part of a subsequent recommendation enabling
the user to respond to one or more offers and access the short term debt
[88] In some embodiments, the recommendations generated by the recommendations and
actions engine 112 may be based on one or more target parameters. The target parameters may
be parameters that define a desired state or level of capital for the entity or a desired state or
level of risk associated with capital management. The target parameters may include a
maximum debt to equity ratio for the entity, maximum weighted average cost of capital,
minimum debt coverage ratio, and/or debtor days or creditor days. An entity may set one or
more target parameters to a desired level depending on the nature of business and risk appetite
of the entity.
[89] In some embodiments, the recommendations and actions engine 112 may be
configured to automatically execute or act on recommendations. For example, if a
recommendation involves accessing short term debt to address a future cash flow shortfall, then
recommendations and actions engine 112 may consider quotes provided by the quoting and
pricing engine 210 to assess which of the quotes for short term debt may be acted on while
maintaining conformity with the one or more target parameters. lf at least one quote for short
term debt may be accessed by the entity while maintaining conformity with the one or more
target parameters, then the recommendations and actions engine 112 may respond to the quote
with a confirmation and accordingly the entity may access short term debt to address a future
cash flow shortfall without any human intervention while managing its risks based on the target
parameters.
[90] In some embodiments, the cash flow forecast engine 110 is configured to reassess the
cash flow forecast for the time frame following actions having been taken by the
recommendations and actions engine 112, or by other components of the capital management
platform or by the user in response to recommendations from the recommendations and actions
engine 112. For example, the baseline cash flow displayed on the graphical display on the user
interface may be updated or overlayed with an adjusted cash flow forecast, which clearly
depicts the impact of the recommendations on the cash flow for the time frame.
[91] In some embodiments, the cash flow forecast engine 110 may be configured to
reassess the cash flow forecast for the time frame based on a prediction as to the probability or
likelihood of payments being received during a particular time frame. For example, the capital
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management component 108 may comprise a network credit score engine 212 configured to
determine a credit score for the entities of the network based on the financial information of the
database 106. In some embodiments, the credit score for any one entity may be based on
transactions of the entity with one or more other entities associated with the capital
management platform 102. The credit score may include an indication as to the probability or
likelihood of an entity paying an invoice on time, for example. The cash flow forecast engine
110 may receive credit scores for payees of the entity, determine a probability of those payees
paying invoices by the due date, and revise or adjust cash flow forecasts based on the
predictions. In some embodiments, the network credit score engine 212 may be configured to
perform the techniques described in PCT/US20 17/045351, the entire content of which is
incorporated herein by reference.
[92] The capital management component 102 may further comprise a retrospective
training logic engine 214 configured to consider the impact the recommendations of the
recommendations and actions engine 112 had on the cash flow forecast of an entity and to
adjust models of the recommendations and actions engine 112 to improve the performance of
the capital management component 108 based on the feedback. For example, the retrospective
training logic engine 214 configured to consider recommendations and action taken in response
to the output of the recommendations and actions engine 112 and its impact on the cash flow
position, determine a measure of effectiveness of the actions, and adjust or vary program logic
or recommendation mode that drives the recommendations and actions engine 112.
[93] Referring now to Figure. 3, there is shown an example screenshot 300 of a visual
display of the capital management tool 104 provided by the capital management component
108. The screenshot 300 illustrates a graphical forecast or prediction relating to invoices and
bills relating to the primary entity. Invoices may comprise future receivables from one or more
counterparties or related entities. Bill may comprise future payment obligations to one or more
counterparties or related entities. Section 302 provides an exemplary 30 day summary of a cash
flow forecast for the primary entity's invoices and bills. Section 304 provides a graphical
illustration of the cash flow forecast over the next 30 days for the entity. Points such below the
x-axis in the graph 304 indicate a negative total cash flow forecast at a particular point in time.
Points above the x-axis indicate a positive cash flow forecast at a particular point in time.
Section 304 comprises a baseline cash flow prediction line 310 indicating the cash flow
position of the primary entity over the next 30 days. Section 304 also comprises a modified
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cash flow prediction line 312. The modified cash flow prediction line 312 indicates a cash flow
prediction for the primary entity if one or more recommendations generated by the
recommendations and actions engine 112 are implemented by the primary entity. The one or
more recommendations generated by the recommendations and actions engine 112 may be
presented to the user in section 306 of the example screenshot 300. Section 306 may also
comprise one or more recommendation interaction user interface components 308 allowing the
user to interact with a recommendation. The interaction may include accepting a
recommendation, designating a date for implementation of a recommendation, or refusing a
recommendation, for example.
[94] Screenshot 300 also illustrates a selectable user input 314 allowing a user to select a
particular account for which a cash flow prediction may be performed by the cash flow forecast
engine 110. By selecting a different account from the selectable user input 314, a user may
visualise a cash flow forecast for a different account for the entity. Screenshot 300 also
illustrates another selectable user input 316 that allows a user to vary the duration over which
the cash flow forecast engine 110 performs the cash flow prediction. A user may select a
different duration of 60 days or 90 days, for example to view a cash flow prediction over a
different timescale.
[95] Screenshot 300 also illustrates some financial data relating to invoices and bills which
provides the basis for the generation ofthe graphs in section 304. Section 318 illustrates a
summary of financial data relating to invoices for the primary entity. In section 318, the
financial data is summarised by the date on which an invoice is due. Section 320 illustrates a
summary of financial data relating to bills for the primary entity. In section 320, the financial
data is summarised by the date on which a bill is due.
[96] Referring now to Figure 4, there is shown a process flow diagram 400 illustrating a
method for forecasting cash flow for an entity, according to some embodiments. The cash flow
forecast engine 110 may predict future cash flow of an entity based on one of several
techniques for predicting future cash flow based on past transactional data. The method of
forecasting cash flow of Figure 4 is an example of one of the methods of cash flow forecasting
according to some embodiments. In some embodiments, the capital management component
108, when executed by one or more processors of the capital management platform 102, is
configured to perform method 400.
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[97] At 402, the cash flow engine 110 determines financial or transactional data associated
with a primary entity. For example, the cash flow engine 110 may query the database 106 to
retrieve financial data, such as historical accounting data or transactional data, relating to the
primary entity. In some embodiments, the financial data is historical time series transactional
data. Each record in the historical time series transactional data may comprise an amount and a
date associated with the amount. In some embodiments, each record in the historical time series
transactional data may comprise an amount, a date associated with the amount and one or more
other entities involved in the transaction. The historical data may provide a basis for
determination of one or more models for prediction of future cash flow. Once a cash flow
prediction model is determined for a particular entity, the model may be varied over time as
more data is made available to improve the accuracy of the cash flow prediction model. The
transactional data may include data relating to one or more of: bank account transactions or
transfers data, invoice data, billings data, expense claim data, cash flow data, quotes related
data, sales data, purchase order data, receivables data, transaction reconciliation data, balance
sheet data, profit and loss data, payroll data, for example. Each record
[98] In some embodiments, the historical transactional data comprises one or more of a
trend component, a seasonality component and a noise component. The trend component
comprises an overall long term trend in the historical transactional data. For example, if the
transactional data relates to sales of a particular product, and the overall demand of the
particular product has been rising over time, then the trend component of the transaction data
will reflect the rising overall demand or sales. The seasonality component comprises variation
in the transactional data over fixed time periods. For example, if the transactional data relates to
sales of a product which is in greater demand in the summer months rather than the winter
months, then the seasonality component may reflect the seasonal variation in the transactional
data relating to the sales. The seasonal component may have a monthly periodicity, weekly
periodicity or daily periodicity, for example. For example, the seasonal components of a
transactional data relating to sales of a business not open during the weekends may have a daily
periodicity reflecting no sales on Saturdays or Sundays. Depending on the nature of the
underlying transactional data, an appropriate periodicity for the seasonal component may be
selected to best reflect historical data and more accurately predict future cash flow for the
primary entity. It will be appreciated that in some situations, there is little or no variation in the
transactional data over fixed time periods, and in such cases, the seasonality component may be
nil. In some embodiments, the historical transactional data comprises a noise component. The
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noise component may reflect variations or changes in transactional data not explained by the
trend or seasonality component. Typically, the noise component will be a very small part of the
transaction data and it may be ignored by the cash flow forecasting engine 110.
[99] In some embodiments, the cash flow forecast engine 110 may model the time series
historical transactional data using an additive model based on the equation "y(t) = a(t) + s(t) +
n". In the additive model, y(t) represents historical time series transactional data, a(t) represents
the trend component, s(t) represents the seasonality component, n represents noise. In some
embodiments, the cash flow forecast engine 110, may model the time series historical
transactional data using an multiplicative model based on the equation "y(t) = a(t) *s(t) + n".
[100] At 404, where a trend component (a(t)) is determined, the cash flow forecast engine
110 determines a regression model for the trend component (a(t)) of a historical time series
transactional data. Various regression analysis techniques, including linear and non-linear
regression models may be used to determine a model for the trend component (a(t)). In some
embodiments, a linear model in the form of "a(t) =intercept+ slope*t" may be used to model
the trend component. The values of"intercept" and "slope" coefficients may be determined by
one or more estimation methods including: least-squares estimation, maximum-likelihood
estimation, for example. With the values of"intercept" and "slope" coefficients determined a
model for the trend component of the historical time series transactional data is obtained that
can be projected into the future to provide future trend estimates.
[101] At 406, based on the regression model ofthe trend component obtained at 404, the
historical time series transaction data is de-trended or in essence, the trend component is
removed from the historical time series transaction data to obtain de-trended data. In some
embodiments, the de-trended data comprises one or more of: the seasonal component and the
noise component. The de-trended historical time series transaction data may be obtained by
dividing the historical time series transaction data by the trend component in embodiments
where a multiplicative model is used. The de-trended historical time series transaction data may
be obtained by subtracting the historical time series transaction data by the trend component in
embodiments where an additive model is used.
[102] At 408, where a seasonality component s(t) is determined, the cash flow forecast
engine 110 determines one or more seasonality coefficients based on the de-trended historical
time series transaction data obtained at 406. Seasonality coefficients define the s(t) function as
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discussed above and reflect a seasonal character or variation of the historical time series
transaction data. Seasonality components may be determined at various periodicities. In some
embodiments, the cash flow forecast engine 110 may initially assume that the seasonality
component has a larger periodicity (for example, a monthly periodicity). Seasonality
coefficients may be calculated on this initial assumption, and an error (or fitness metric) in the
calculated seasonality coefficients fitting the historical time series transactional data may be
calculated at 410 to assess whether the assumed periodicity appropriately models the seasonal
trend in the historical time series transactional data. If the calculated error or fitness metric does
not appropriately model the seasonal components, or is above a certain error threshold, then a
different, shorter periodicity may be assumed for calculation of the seasonality coefficients.
[103] The seasonality coefficients may be determined at 408 by processing the de-trended
time series transactional data to obtain a coefficient for each seasonal cycle (month of year,
week of month, day of week) that best models the seasonal component. The seasonality
coefficients may be obtained by averaging the de-trended time series transactional data over a
period of time. For example, to obtain seasonality coefficients for the month of January (when
the periodicity is assumed to be a month of the year), the de-trended time series transactional
data for the month of January may be averaged over a certain period of time to obtain the
seasonality coefficient for the month of January. Similarly seasonality coefficients for other
months may be calculated for the entire year. The seasonality coefficients for other smaller
periodicities (week of month, day of week) may be determined using the same approach.
[ 104] Having determined a regression model for the trend component at 404 and the
seasonality coefficients at 408, a fitness metric or an error metric is determined at 410. The
fitness metric or error metric indicates how well the determined trend component and
seasonality coefficients model the historical time series transactional data. The fitness metric or
error metric may be calculated by calculating the difference between the historical time series
transaction data and the modelled trend component and seasonality coefficient. A mean,
variance or other statistical measure of the difference may be used as the fitness metric or error
metric.
[105] At 412, the cash flow forecast engine 110 determines whether the fitness metric
calculated at 410 is above a certain fitness threshold indicating an acceptable degree of fitness
and accuracy of the trend model and seasonality coefficients. If the fitness metric calculated at
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410 is below the threshold, then the cash flow forecast engine 110 may reduce the periodicity
of the seasonality component and recalculate the seasonality coefficients at 410 using the
smaller periodicity to obtain seasonality coefficients that better model the historical time series
transactional data. In some embodiments, the historical time series transactional data may
comprise a combination of seasonal patterns. For example, the historical time series
transactional data may have a pattern for a month of the year, another pattern for week of the
month and another pattern for day of the week. In such embodiments, seasonality coefficients
for the various different periodicities may be calculated to most accurately model the historical
time series transactional data.
[106] At 414, based on the determined trend model at 404 and the seasonality coefficients at
408, cash flow forecast engine 110 determines future cash flow predictions. The steps 402 to
412 may be performed separately for different categories of historical transaction data records
for an entity. For example, steps 402 to 412 may be separately performed for an entity's sales
transaction data, expenses transaction data, payroll transaction data, for example. The historical
transaction data may be appropriately characterised and sectored or categorised to individually
model each sector or category. At step 414, the output of each model determined for an entity
may be projected into the future to determine an overall future cash flow prediction for the
entity.
[107] In some embodiments, the cash flow forecasting engine 110 may generate the cash
flow forecast based on method 1200 of Figure 12 and/or method 1300 of Figure 13, as
described in more detail below. For example, the cash flow forecast engine 110 may determine
the future cash flow predictions based at least in part on determined recurring transactions.
[108] At 416, in some embodiments, the cash flow forecast engine 110 may query the
network credit score engine 212 that may be configured to perform the techniques described in
PCT/US20 17/045351 to obtain network credit score for counterparties or related entities or
third parties. Based on the obtained network credit scores, the cash flow prediction determined
at 414 may be varied or adjusted to take into account the financial position of counterparties or
related entities or third parties and obtain a more accurate cash flow prediction for the entity.
The network credit score engine 212 may be configured to determine a probability or likelihood
of a creditor paying an invoice within a particular payment period and this information may be
used to recalibrate the cash flow prediction for the primary entity. For example, if it is
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determined that the creditor is unlikely to pay an invoice issued by the primary entity by a due
date, the cash flow forecast engine 110 will factor this into the prediction and not expect to
receive that payment on the due date.
[109] The various modules ofthe cash flow forecasting engine 110: Payables Accrual Logic
Engine 200, Recurring Cash Account Logic Engine 202, Tax Prediction Engine 204, Payroll
Prediction Engine 206, Receivables Accrual Logic Engine 208 may implement the process
flow of Figure 4 to determine a cash flow forecast of their respective transactional domains.
Output from each of the modules within the cash flow forecasting engine 110 may be combined
by the cash flow forecasting engine 110 to determine an overall cash flow forecast for an entity.
[110] Figure 5 illustrates first and second charts 502, and 504, respectively, obtained by
implementing the process flow 400 of Figure 4 using test historical transaction data. The X-axis
of both charts 502 and 504 corresponds to a historical transaction amount or a predicted
transaction amount and theY -axis of both charts 502 and 504 corresponds to a date. Line
graphs 506 and 510 in charts 502 and 504, respectively, represent actual transactional data for a
particular entity. Line graphs 508 and 512 in charts 502 and 504, respectively, represent
predicted transactional data for a particular entity based on historical transactional data. As is
observable from charts 502 and 504, the predicted transaction data and the actual transaction
data closely align during a significant portion of the time periods in charts 502 and 504.
Predictions for several categories of transaction data for a particular entity may be combined to
obtain an overall cash flow prediction for an entity as illustrated in section 304 of Figure 3.
[111] Figure 6 is a block diagram depicting an example capital management platform 600,
according to some embodiments. The example capital management platform 600 may provide
accounting tools to a particular entity managing accounting for one or more businesses. The
example capital management platform 600 may include a practice studio 610 that allows an
entity to manage one or more businesses and an organization access component 650 that
provides a business with tools for managing accounting data for that particular business. The
practice studio 610 may include a practice profile management component 612, a practice staff
management component 614, an online training component 616, a practice management
component 618, a partner resources component 620, a report packs setup component 622, and a
work papers component 624. The practice studio 610 may be in communication with core
features 630. The core features 630 may include an accounting and payroll component 632
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(including capital management component 108), a community component 634, a
billing/subscription management component 636, a notifications centre component 638, a user
profile management component 640, and a login component 642. The organization access
component 650 may be in communication with the core features 630. The practice studio 610
and core features may be accessed by an entity using login component 642. The features of the
practice studio 610 provide a suite of tools for accountants to interact with their clients and
manage their practices. The core features 630 provide the core functionality and user tools
common to both accountants and businesses. The organization access component 650 provides
a user interface for individual businesses to access their data.
[112] The accounting and payroll component 632 provides the general ledger for
organizations. The general ledger may be integrated with the organization's payroll, bypassing
the separate step of entering payroll data into the general ledger each pay period. The
accounting and payroll component 632 accesses banking data for each client business. The
banking data may be imported either through a bank feed or a user- or accountant-created
document. The accounting and payroll component 632 may also communicate with third-party
tools via an application protocol interface (API).
[113] The capital management component 108 enables generation of a capital management
or cash flow forecast predictions and effective actions for improving capital position. The
capital management component 108 may interact with the accounting and payroll component
632, the billing/subscription management component 636, and the notifications centre 638, for
example, to perform data processing operations on financial data, including data corresponding
to inflow and outflow financial transactions.
[114] Figure 7 is a flowchart of a method 700 for managing capital for a primary entity,
according to some example embodiments. In some embodiments, the capital management
component 110, when executed by one or more processors of the capital management platform
102, is configured to perform method 700.
[ 115] At 702, the cash flow forecasting engine 110 determines a cash flow forecast for the
primary entity over a certain period of time in the future. The period of time may be 30 days,
60 days, 90 days or 6 months or a year for example. The cash flow forecasting engine 110 may
generate the cash flow forecast based on the process flow diagram of Figure 4. The generated
cash flow forecast may be presented to a user in the form of a graph 304 as illustrated in Figure
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3. In some embodiments, the cash flow forecasting engine 110 may generate the cash flow
forecast based on method 1200 of Figure 12 and/or method 1300 of Figure 13, as described in
more detail below.
[116] At 704, a sub-time frame capital surplus or capital shortfall is determined by the cash
flow forecasting engine 110. This may involve identifying time periods wherein the primary
entity may have excess cash (cash flow surplus) that could be used for specific purposes or
alternatively time periods wherein the primary entity will experience cash flow shortfalls
requiring the primary entity to take corrective action in advance or in response to the cash flow
shortfall. The sub-time frames identified may have a start date and an end date.
[117] At 706, the recommendations and actions engine 112 may take into account the subtime
frames of capital surplus and shortfall at 704 to determine recommendations or
suggestions to improve the capital or cash flow position of the primary entity in response to the
predicted periods of cash flow surplus or shortfall. The recommendations and actions engine
112 may comprise one or more machine learning models trained to accept as input the cash
flow forecast generated at 702, other financial transaction data or balance sheet data of the
primary entity stored in database 106. The recommendations and actions engine 112 may also
be configured to take into account one or more financial target parameters. The target
parameters may be parameters that define a desired state or level of capital for the entity or a
desired state or level of risk associated with capital management. The target parameters may
include a maximum debt to equity ratio for the entity, maximum weighted average cost of
capital, minimum debt coverage ratio, and/or debtor days or creditor days. An entity may set
one or more target parameters to a desired level depending on the nature of business and risk
appetite of the entity.
[118] The recommendations and actions engine 112 may comprise one or more machine
learning models comprising one or more: artificial neural networks, intelligent agents,
nonparametric models, support vector machines, probabilistic models, for example. Each of the
various machine learning frameworks may be initially trained or configured using a training
dataset comprising cash flow forecasts for an entity, sub-time frames or cash flow shortfall or
surplus, financial target parameters and one or more recommendations for each sub-time frame
of capital shortfall or surplus. The trained machine learning models at 706 may take into
account each sub-time frame of capital shortfall or surplus for the primary entity, the target
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financial parameters from the financial entity and generate recommendations for the primary
entity to improve its cash flow at each sub-time frame of cash flow shortfall or surplus.

CLAIMS
1. A computer-implemented method comprising:
determining a dataset of transactions occurring during a first time period;
determining at least a one subset of related transactions from the dataset of
transactions by:
identifying a first group of transactions from the dataset of transactions,
wherein the transactions of the first group have one or more common attributes;
determining a interval for each of the at least one subsets, each of the intervals
being indicative of a periodicity of the respective one of the at least one subsets; and
generating a model of periodic transactions, the model including, for each of
the at least one subsets: (i) the interval; and (ii) at least one of the common attributes.
2. The method of claim 1, wherein identifying a first group of transactions from the
dataset of transactions comprises identifying transactions between a first entity and one or more
second entities.
3. The method of claim 1, wherein identifying a first group of transactions from the
dataset of transactions comprises identifying transactions between a first account associated
with a first entity and one or more second entities.
4. The method of claim 1, wherein identifying a first group of transactions from the
dataset of transactions comprises identifying transactions with substantially similar payment
amounts.
5. The method of any one ofthe claims 1 to 3, wherein determining the at least one
subset of related transactions from the dataset of transactions further comprises:
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identifying a second group of transactions from the first group of transactions,
wherein the second group of transactions each relate to transactions with substantially similar
payment amounts.
6. The method of claim 4, wherein determining the at least one subset of related
transactions from the dataset of transactions further comprises:
identifying a second group of transactions from the dataset of transactions,
wherein the second group of transactions each relate to transactions between a first entity and
one or more second entities.
7. The method of any one of claims 4 to 6, further comprising:
rounding the payment amount of each of the first group of transactions or of the
dataset of transactions prior to identifying the second group of transactions.
8. The method of claim 1, wherein the one or more common attributes comprise one or
more of: (i) account name; (ii) contact; (iii) payment amount.
9. The method of any one of the preceding claims, wherein determining the interval for
each of the at least one subsets comprises:
determining that a regularity of an occurrence of the related transactions of the
at least one subset of transactions throughout the first time period meets a regularity threshold.
10. The method of claim 9, wherein determining that a regularity of an occurrence of the
transactions of the at least one subset of transactions throughout the first time period meets a
regularity threshold comprises:
determining, for each related transaction of the at least one subset of
transactions, a number of days between the related transaction and a next occurring related
transaction based on a date of the related transaction and the next occurring related transaction;
determining a standard deviation value of the numbers of days for the at least
one subset of transactions; and
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determining that the standard deviation value meets the regularity threshold.
11. The method of any one of the preceding claims, wherein determining the interval for
each of the at least one subsets comprises comparing a distribution of individual intervals of the
related transactions with one or more distribution models indicative of periodicity to determine
a best fit.
12. The method of any one ofthe preceding claims, further comprising:
determining that a time span covered by the related transactions of the at least
one subset throughout the first time period meets a coverage threshold.
13. The method of any one of the preceding claims, further comprising predicting one or
more instances of future transactions using the model.
14. The method of claim 13, wherein the model predicts, for each recurring transaction,
one or more of the following attributes of the transaction:
(i) payment amount;
(ii) regularity of payment;
(iii) day(s), week(s), month(s), and/or year(s) on which payment is predicted to be
paid;
(iv) account to and/or from which payment is predicted to be made; and
(v) contact to and/or from which payment is predicted to be made.
15. A system comprising:
one or more processors;
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a memory in communication with the one or more processor, the memory comprising
program code which when executed by the one or more processors configures the one or more
processors to perform the method of any one of claims 1 to 14.
16. A machine-readable medium storing computer readable code, which when executed
by one or more processors is configured to perform the method of any one of claims 1 to 14.
17. A computer implemented method comprising:
assessing financial data associated with a primary entity during a time frame;
predicting recurring transactions during the time frame;
determining a baseline cash flow forecast for the primary entity for the time
frame based at least one the predicted recurring transactions;
determining predicted capital surplus and/or capital deficit during the time
frame based at least on the baseline cash flow forecast;
determining one or more sub-time frames of the time frame where cash flow is
predicted to be in surplus or deficit;
based on the determined sub-time frames, generating by a recommendation
engine, one or more recommendations to improve capital management for the primary entity.
18. The computer implemented method of claim 17, wherein predicting recurring
transactions during the time frame comprises performing the method of any one of claims 1 to
14.
19. The method of claim 17 or claim 18, wherein determining the baseline cash flow
forecast comprises:
determining a trend component based on the financial data associated with the
primary entity for that time frame;
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determining a first seasonality component based on the financial data; and
projecting the trend component and the first seasonality component to the time frame
to determine the baseline cash flow forecast.
20. The method of claim 19, wherein determining the baseline cash flow forecast
compnses:
determining the first seasonality component based on a first periodicity in the
financial data;
determining a fitness metric by comparing the financial data with the trend model and
the seasonality component;
responsive to the determined fitness metric being below a predetermined fitness
threshold, determining a second seasonality component based on a second periodicity in the
financial data; and
projecting the trend component and the second seasonality component to the time
frame to determine the baseline cash flow forecast.
21. The method of any one of claims 17 to 20, further comprising generating a cash flow
forecast tool within a graphical user interface (GUI) of a display device, wherein the cash flow
forecast tool depicts a representation of the baseline cash flow forecast and the one or more
recommendations.
22. The method of claim 21, further comprising:
in response to detecting user selection of one or more of the one or more suggestions
using the cash flow forecast tool, adjusting the financial data in accordance with the selected
one or more suggestions;
determining a modified cash flow forecast for the time frame based on the adjusted
financial data; and
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depicting a representation of the modified cash flow forecast within the cash flow
forecast tool.
23. The method of any one of claims 17 to 22, further comprising:
automatically executing at least one of the recommendations based on target
parameters;
analysing the automatically executed recommendations to determine whether the
executed capital management recommendations mitigated the forecasted one or more cash flow
shortfall periods or one or more cash flow excess periods; and
revising the recommendation engine based on the analysis.
24. A system comprising:
one or more processors;
a memory in communication with the one or more processor, the memory comprising
program code which when executed by the one or more processors configures the one or more
processors to perform the method of any one of claims 17 to 23.
25. A machine-readable medium storing computer readable code, which when executed
by one or more processors is configured to perform the method of any one of claims 17 to 23.